Effective knowledge management (KM) in a knowledge-intensive working environment requires an understanding of workers' information needs for tasks, (task-needs), so that they can be provided with appropriate codified knowledge (textual documents) when performing long-term tasks. This work proposes a novel profiling technique based on implicit relevance feedback and collaborative filtering techniques that model workers' task-needs. The proposed profiling method analyses variations in workers' task-needs for topics (i.e., topic needs) in a topic taxonomy over time. Variations in the topic needs of similar workers' are used to predict variations in a target worker's topic needs and adjust his/her task profile accordingly. Experiment results suggest that considering variations in the topic needs of similar workers' during the profile adaptation process is effective in improving the precision of document retrieval.